Introduction and Literature review Modelling film box office revenue has long been of interest to marketers and business researchers (see e.g.

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1 Introduction and Literature review Modelling film box office revenue has long been of interest to marketers and business researchers (see e.g. Kerrigan, 2010 for an overview). The aim of many of such studies has tended to be either to identify the predictors of a films box office performance in its opening weekend or of its total box office performance. Films are experience goods and hence consumers have to rely on quality signals to guide their choices. Such quality signals include those created by the film s producers either directly through advertising or indirectly through the design of the film (including casting decisions, choice of director, BBFC certification, genre and so on). Independent quality signals are provided by reviews and by word of mouth. The quality signals investigated so far include the genre of the movie (e.g. Litman, 1983), star names in the cast (e.g. Elberse, 2007), star directors (e.g. Simonet, 1980), advertising expenditure (e.g. Prag and Casavant, 1994), awards (e.g. Nelson et al., 2001), and critical reviews (e.g. Eliashberg & Shugan, 1997). Most of these studies have investigated either the effect of these quality indicators on either total revenue or opening revenue. More recent studies have investigated the relationship between film characteristics, launch decisions, opening revenue and total revenue and have adopted a structural equation modelling / path modelling approach. For example, Hennig-Thurau et al. (2006) tested the impact of a movie s design and of critical reviews on the revenue earned on opening weekend and total revenue using a structural equation model. Predictors of film performance, such as the presence of stars, the number of screens a film opens on as well as measures of familiarity such as if the film was based on a book were used as indicators of a reflective latent variable they termed studio actions. They found that although studio actions affect opening revenue, critics reviews had a greater impact on total revenue. In other words, marketing effort was found to have a direct effect only on opening revenue. A movie s long term performance depends on its quality (proxied by critics reviews), and on its opening performance. Hennig-Thurau et al. (2007) explored the impact of a similar set of explanatory variables on opening revenue, total revenue and profitability using a fully disaggregated model (i.e. without conceptualising studio actions as a latent variable). Treating studio actions and film quality as reflective latent variables (e.g. Hennig- Thurau et al., 2006; Hadida, 2010) reduces the number of relationships to be estimated. However this approach assumes that the different constituent elements are correlated with each other. Whilst this may be true for some variables, such as movie reviews, it is not necessarily the case for other variables. There is some evidence that movie reviews are correlated with each other (e.g. Boor, 1992) which suggests that reviews could be treated as a reflective construct. However, the components of a studio action construct need not necessarily be correlated with each other. Whilst it might be the case that a film which has a star name director will also have star name actors in the cast, it doesn t necessarily follow that the movie will be a sequel or be based on a book. It maybe that movie will have a star director but a little known cast (or vice versa). The extent to which the indicators are correlated will also depend on how they are operationalised. For example, a number of methods have been employed for measuring star power (such as the revenue earned by the star s previous film, whether or not the star appeared in one of the previous year s top 10 films, number of nominations or awards won and position of published star power lists). It could be argued that such a construct might be better regarded as a formative, rather than reflective variable (which would also allow the impact of individual variables to be identified) or not regarded as a construct at all, but as a set of independent variables. The uncertainty surrounding box office performance in the movie industry is summed up in screenwriter William Goldman s (1983) often quoted comment that No-one knows 1

2 anything in this business. One source of this uncertainty could be degree of heterogeneity across films; in other words the impact of a star name or of the number of opening screens may vary across film titles. As Albers (2010) argues, performance driver studies should also test the robustness of their results by testing for the effects of unobserved heterogeneity across cases. One way to do this is to employ methods like finite mixture regression which simultaneously determines the number of subgroups and produces regression coefficients for each subgroup. However, few studies of film performance have thus far adopted this approach. Hennig-Thurau et al., (2006) and Antipov and Pokryshevskaya, (2011) are exceptions. The former report results from a single equation finite mixture regression alongside their CBSEM results; the latter have employed a single equation models. The Model This paper adopts a similar model to that employed by Hennig-Thurau et al., (2006) for the UK film market. Rather than treat film characteristics as indicators of a latent studio actions variable, a disaggregated model is estimated. There are other variables which could be included in the model, in particular whether the film is distributed by one of the major studios. However, the aim of the study is to test whether the results reported in prior studies (in particular the use of formative constructs) can be replicated when alternative conceptualisations are used. A different estimation method, partial least squares (PLS), is employed. PLS has one distinct advantage over covariance based structural equation models here: it can be extended to test the robustness of the results by applying a form of finite mixture regression, known as FIMIX-PLS. It also makes fewer assumptions about the distribution of the data. The model to be estimated is given in figure 1 below. Figure 1. Conceptual model 2

3 Variable definitions The model is tested on a sample of 214 movies released in the UK in The sample is restricted to movies which ran for at least four weeks. The opening revenue, total revenue and opening screens data were obtained from AC Nielsen EDI. To reduce the degree of correlation between opening revenue and total revenue, we follow Hennig-Thurau et al. (2006) and use total box office revenue minus opening box office revenue as the measure of total box office revenue. We also transform these three variables using natural logarithms to reduce the skewness in the data. A number of approaches have been used in the past to measure the impact of stars and directors, such as star power lists, box office performance of last film or involvement in a top 10 film in the previous year. All of these imply that a past film s performance was entirely due to the star s or director s involvement. The career number of wins and nominations for Academy Awards and British Academy of Film and Television Arts (BAFTA) awards as the basis for the star power measure. We take the square root of the number of nominations and wins as our measure as it is likely that the impact of an additional award will decrease as the total number of awards won increases. A film was classed as a family film if it received a U or PG certificate from the British Board of Film Classification. The IMDB was also used to identify sequels, movies based on books and movies based on famous people s lives (the personality variable). The movie quality variable uses the average of the critics scores from nine national newspapers on a 10 point scale (the reviews were obtained from the website of one of the newspapers, The Daily Telegraph). The movie quality measure for a given movie is the average score from the newspapers which carried a review of that movie (the numbers of reviews and the newspapers which carried them varies from movie to movie). Results The estimated coefficients for the model in figure 1 are presented in table 1 below (estimates were obtained from SmartPLS, Ringle, Wende and Will, 2005). What Hennig-Thurau et al. (2006) found for the US market also holds for the UK market. Film characteristics are significant predictors of opening screens and opening revenue, but do not have a direct effect on total revenue (with the exception of the family film variable). Reviews on the other hand, do have an effect on total revenue and on opening revenue. Table 1. Estimate pathway coefficients Coeff t stats R 2 actor opening screens ** opening screens 0.27 director opening screens * opening revenue 0.90 family film opening screens * total revenue 0.93 sequel opening screens * book opening screens personality opening screens *** actor opening revenue director opening revenue family film opening revenue sequel opening revenue ** book opening revenue personality opening revenue

4 average review opening revenue *** opening screens opening revenue *** actor total revenue director total revenue family film total revenue * sequel total revenue book total revenue personality total revenue average review total revenue * opening revenue total revenue *** *,** and *** denote significant at 5%, 1% and 0.1% level. t statistics derived from bootstrapped standard errors using 500 samples. In other words, marketing effort (either in the design of the movie or its promotion at launch) has a short-term impact, affecting opening revenue but not directly influencing total revenue. Film quality, as measured by reviews on the other hand has a significant effect on both opening revenue and on total revenue. Whilst the model appears to be able to explain Opening Revenue and Total Revenue well (the R squared values are 0.90 and 0.93 respectively), it is less able to explain Launch Decisions (the R squared is 0.27). In general, as Albers (2010) argues, performance driver studies should test the robustness of their results by testing for the stability of the estimated coefficients. Within PLS, testing the stability of the estimated coefficients can be accomplished using FIMIX-PLS to identify homogenous segments within the data set and produces pathway coefficients for each segment (see e.g. Hahn et al., 2002; Ringle, Wende and Will, 2010). Again, SmartPLS (Ringle et al., 2005) was used to run the model. As in any clustering procedure, a key decision to be taken is the number of segments to create. As Sarstedt et al. (2011) note, statistical model selection criteria used in finite mixture modelling such as Akaike s Information Criterion (AIC), Bayesian Information Criterion (BIC), consistent AIC (CAIC) and the normed entropy criterion can also be applied to the assist the selection of the number of segments to be retained. The closer the entropy value is to 1 the better the separation between the clusters (Hahn et al., 2002). The AIC, BAIC, CAIC and entropy values are shown in table 2. Table 2. Goodness of fit measures No of segments: AIC BIC CAIC Entropy The two segment solution shows the lowest values for AIC, BIC and CAIC and highest value for entropy. That said, other studies using FIMIX-PLS (e.g. Ringle et al., 2010; Hahn, 2002) have based segments on entropy levels of between 0.43 and 0.5. FIMIX PLS results for the two segment solution are shown in table 3 below. The two segments show a number of big differences. 4

5 Table 3. FIMIX-PLS results Segment 1 Segment 2 actor opening screens director opening screens family film opening screens sequel opening screens book opening screens personality opening screens actor opening revenue director opening revenue family film opening revenue sequel opening revenue book opening revenue personality opening revenue average review opening revenue opening screens opening revenue actor total revenue director total revenue family film total revenue sequel total revenue book total revenue personality total revenue average review total revenue opening revenue total revenue Segment 1 is the smaller of the two, containing 14% of the sample (30 films) and so the results should be interpreted with some caution. That said, the results suggest that that results based on the whole dataset are not stable; rather the impact of film characteristics and of reviews, differ across the two segments. Conclusions The results suggest that findings for the US market also hold for the UK; marketing effort (in terms of both the design of a movie and its promotion at launch) can only influence short-term performance. Furthermore the finding is robust to the method of analysis used (PLS versus CBSEM). However, treating film characteristics as separate variables, rather than as indicators of a reflective construct, allows the impact of individual film attributes to be identified. The FIMIX-PLS results however, suggest that within such results there is a degree of variability in the relationship between movie design, launch decisions, movie quality and opening revenue and between movie quality, opening revenue and total revenue. The variability in the path coefficients offers a clear illustration of the degree of uncertainty inherent in the movie business. Future research could investigate this issue with a larger data set, to determine whether the subgroups of films are robust, or whether the characteristics of the segments also varies across data sets. The impact of the power of both the distributor and the distributor on securing screens at launch would also be a useful extension to the paper. 5

6 References Albers, S. (2010). PLS and Success Factor Studies in Marketing. In Esposito Vinzi, V., Chin, W., Henseler, J. & Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications, (pp ). Berlin: Springer-Verlag. Antipov, E. and Pokryshevskaya, E. (2011) Accounting for latent classes in movie box office modelling, Journal of Targeting, Measurement and Analysis for Marketing, 19, Boor, M. (1992). Relationships among ratings of motion pictures by viewers and six professional movie critics, Psychological Reports, 70, Elberse, A. (2007). The power of stars: do star actors drive the success of movies?, Journal of Marketing, 71, Goldman, W. (1983). Adventures in the Screen Trade, New York, Warner Books. Hadida, A. (2010). Commercial success and artistic recognition of motion picture projects, Journal of Cultural Economics, 34, Hahn, C., Johnson, M., Herrman, A. and Huber, F. (2002). Capturing customer heterogeneity using a finite mixture PLS approach, Schmalenbach Business Review, 54, Hennig-Thurau, T., Houston, M. and Sridhar, S. (2006). Can good marketing carry a bad product? Evidence from the motion picture industry. Marketing Letters, 17, Hennig-Thurau, T., Houston, M. And Walsh, G. (2007). Determinants of Motion Picture Box Office and Profitability: An Interrelationship Approach, Review of Managerial Science, 1, Kerrigan, F. (2010) Film Marketing, Oxford, Butterworth-Heinemann. Litman, B. (1983). Predicting success of theatrical movies: an empirical study, Journal of Popular Culture, 16, Nelson, R., Donihue, M., Waldman, D. and Wheaton, C. (2001). What s an Oscar worth? Economic Inquiry, 39, 1-16 Prag, J. and Casavant, J. (1994). An empirical study of the determinants of revenues and marketing expenditures in the motion picture industry, Journal of Cultural Economics, 18, Ringle, C., Wende, S. and Will, A. (2005) SmartPLS 2.0 (Beta), Ringle, C., Wende, S. and Will, A. (2010). Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples, In Esposito Vinzi, V., Chin, W., Henseler, J. & Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications, (pp ). Berlin: Springer-Verlag. 6

7 Simonet, T. (1980). Regression analysis of prior experience of key production personnel as predictors of revenues from high-grossing motion pictures in American release, New York: Arno Press. 7